Senior engineers of ours who map where AI brings a measurable result in your product, your operation and your delivery.
One owner of one problem, for SaaS teams and IT departments with systems of their own.
Not in your backlog or a ticket queue. They learn how your work runs before deciding what to build.
A specification (what the change must do and how to check it), written with your people before any code.
They own it from the first conversation to the system running, and operate it in production. It ends as a documented, tested system your team operates.
Your forward deployed engineer runs discovery: a working week with your team, not a slide deck.
What they look at: high-volume work that follows a pattern, the data that exists, what counts as correct, and what an error costs each way.
What they look for: work your team does by hand that the factory or an agent could take over.
You leave with a map of where AI brings a measurable result, ranked by result and by what it takes to get there.
The forward deployed engineer picks the front for each problem. The same engagement covers all three.
Features, retrieval, copilots and agents inside your product, built with the factory on your repository.
The factory →Agents in your processes: conversational, decision, marketing and sales, product, process. Each is specified before code, measured on your real cases, and run every day.
The agents →The AI Software Factory: specifications in, verified code out. Its QA and legacy lines protect and modernize what already runs.
The factory →The pod is what you hire, month to month, working through the factory, an agent, or both.
One forward deployed engineer, inside your team: your channels, your repository, your standups.
One deployment strategist, a senior lead shared with other clients. They set up the factory on your architecture, keep it tuned, and carry what one engagement learns to the next.
The factory, or the agent, running on your repository and your CI: AI-generated code, tests, and an independent check that did not write the code.
They built production systems for years before AI, so their review of every change comes from experience.
Writing code by hand is the exception. They direct the AI daily and know when to reject what it produced.
They read a process, a queue and a margin, and tell you where AI will not bring a measurable result.
They make changes that pass the tests of a large codebase they did not write.
They explain trade-offs to non-technical stakeholders and disagree when that is the right call.
A 45-minute working session, then a discovery week with your forward deployed engineer inside your team. Together we write three specifications from the discovery map, each with its observable result defined.
Factory, weeks 2 to 4: the three specs become pull requests your team approves. Agents, weeks 2 to 7: one agent on a real queue, accuracy measured on your cases.
The pod keeps working inside your team. The map is updated as results come in, and you decide the next problem, or whether to stop.
We take them in the first week. Every month after is measured against them.
The system running in production.
The AI map of your operation: what to build next, in what order, and why.
The specification and the tests that prove it, running in your CI.
The code and the repository, including history.
The IP. No license back to us and no runtime dependency on anything of ours.
The engineering brain: a record of every decision, rule and test, connected and kept in your repository, so whoever continues the work starts with context.
If you later build a Company Brain, one connected record of how your company works, this is already part of it.
Done means: the observable result defined in the working session, measured.
One 45-minute working session: we map where AI brings a measurable result, write the first specifications with you, and define the observable result.